Paper on the development of a general mathematical framework to guide R&D towards new diagnostics
20 Jun 2022
Two-stage lot quality assurance sampling framework for monitoring and evaluation of neglected tropical diseases, allowing for imperfect diagnostics and spatial heterogeneity
REASSURED (real-time connectivity, ease of specimen, affordable, sensitive, specific, user-friendly, rapid and robust, equipment free or simple/environment friendly, deliverable to end users) diagnostics are highly needed tools in variety of interventions to control or eliminate infectious diseases in both human and animal populations. While most traditional diagnostics are often labor intensive and lack both sensitivity and reproducibility, the new more performant diagnostics remain costly and laboratory based. To steer the R&D of new diagnostics tools diseases specific target product profiles (TPP) are being developed. These TPPs describe the minimal and ideal requirements for various diagnostic needs (e.g., simplicity, performance, price of the test) related to disease specific use-cases (initiate, monitor and evaluate a disease control program vs. verify whether transmission of diseases has been interrupted). A particular challenge for the committees assigned to write-up these TPPs is to determine the required diagnostic performance (sensitivity and specificity), through-put and cost per test. As a response to this need for more evidence-based TPPs, the Laboratory of Parasitology (Faculty of Veterinary Medicine, Ghent University) researched the minimum diagnostic attributes in more depth. For this, it has developed a mathematical framework that allows to assess the impact of the diagnostic performance on the decision-making (e.g. overtreatment vs. undertreatment), the highest possible cost per test and the minimum sample throughput. Although the framework is illustrated for large scale deworming programs aiming to control intestinal worm infections in children (the Laboratory chairs the TPP committee for this specific use-case), it can be applied to any infectious diseases for which decisions are made at the population level.